When operating a system having multiple components, it is often desirable to have an awareness of the current state of the health of the system and its components. Such awareness is often obtained using diagnostic tools, where the diagnostic tools evaluate the system to identify faulty components. Although knowledge of faulty components assists a user in maintaining the system, it does not provide information about future performance of the system. Such projections are typically obtained using a prognostic tool.
In conventional software tools, diagnosis and prognosis are handled separately. Diagnosis tools use rules, case bases, or logic models to provide diagnostic recommendations. These approaches are not suitable for expressing uncertainty, which is inherent in prognosis. Current prognosis solutions are limited to individual components or simple subsystems (e.g., bearings, turbine disks, electric motors, and batteries). The reasoning in these systems is typically customized to the application and often based on simple heuristics.
For example, a paper entitled, “Prognostics, Real Issues Involved with Predicting Life Remaining,” by S. Engel et. al, presented at the Proc. IEEE Aerospace Conf. 2000, describes in general terms the prognosis problem. The paper presents prognosis within a probabilistic framework and defines key issues involved in applying a probabilistic approach to prognosis. However the probabilistic approach is theoretical in nature and the paper does not provide specific solutions or algorithms. Additionally, the paper does not describe how to implement the probabilistic approach to a practical complex system. In particular, the paper does not present a way of integrating the diagnosis with prognosis. Although it mentions Bayesian Network (BN) as a possible avenue of implementation, it lacks discussion of a specific algorithm capable of computing probability of component failure given usage and mission, present health, and health-trend information.
Additionally, a paper entitled, “An Open Systems Architecture for Prognostic Inference during Condition-Based Monitoring,” by G, Provan and presented at the Aerospace Conference 2003, describes an open systems architecture representation that is critical to a high-level analysis of prognosis. The paper specifies a generic prognosis module, the inputs and outputs to it, measures of remaining useful life and the importance of how a component will be used. However, the paper is very unclear about the details of its architecture and does not provide any insight into the exact nature of how the observations can be integrated into a prognostic framework.
There are several solutions that can be found in the literature that focus on applying the general framework for a particular application and on developing prognostic solutions for specific subsystems. By way of example, the first reference below belongs to the former category and the following two references belong to the latter category.
A paper entitled, “Prognostic Enhancements to Gas Turbine Diagnostic Systems,” by C. S. Byington, M. Watson, M. J. Roemer, T. R. Galie and J. J. McGroarty, that was presented at the Proc. of IEEE Aerospace Conference 2003, describes the general framework for a specific application (gas turbine engine diagnosis). Although observations from sensors and historical data are used, the system described in this paper does not include physics models and mission requirements.
Another paper, entitled, “eSTORM: Enhanced Self Tuning On-board Real-Time Engine Model,” by T. Brotherton, A. Volponi, R. Luppold & D. L. Simon, and published at the Aerospace Conference 2003, describes a method for on-line diagnostics and prognostics. The method improved upon a physics-based model called STORM, with an empirical neural network such that modeling errors during on-line functions are mitigated. The model described in this paper was developed for an aircraft engine, with results being specific to the aircraft engine. The paper focused on a narrow aspect of trending for a subsystem and did not address the crucial aspect of how various components for prognosis can be integrated into a single framework.
A paper entitled, “Nonparametric Modeling of Vibration Signal Features for Equipment Health Monitoring,” by S. W. Wegerich, describes a method for modeling vibrations of systems from data. It also provided a method for evaluating if the vibrations are predictive of impending failures and provides an approach to compute “Useful Life Remaining” of equipment based on the residual errors computed from the vibration characteristics. The modeling described in the paper is very narrow as it applied to a specific component of a subsystem.
No system heretofore devised combines diagnostic tools with prognostic tools. Thus, a continuing need exists for a systematic solution, which combines diagnosis with prognosis and that is application independent and can be scaled to complex systems.